






"""simple docstring"""
from random import randint
from tempfile import TemporaryFile

import numpy as np


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: str ,     __snake_case: Dict  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    0
       if start < end:
              __magic_name__      				=    randint(__snake_case ,     __snake_case  )
              __magic_name__      				=    a[end]
              __magic_name__      				=    a[pivot]
              __magic_name__      				=    temp

              __magic_name__				,  __magic_name__      				=    _in_place_partition(__snake_case ,     __snake_case ,     __snake_case  )
              count += _in_place_quick_sort(__snake_case ,     __snake_case ,     p - 1  )
              count += _in_place_quick_sort(__snake_case ,     p + 1 ,     __snake_case  )
       return count


def 							A						(				__snake_case: Union[str, Any] ,     __snake_case: Optional[Any] ,     __snake_case: Any  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    0
       __magic_name__      				=    randint(__snake_case ,     __snake_case  )
       __magic_name__      				=    a[end]
       __magic_name__      				=    a[pivot]
       __magic_name__      				=    temp
       __magic_name__      				=    start - 1
       for index in range(__snake_case ,     __snake_case  ):
              count += 1
              if a[index] < a[end]:  # check if current val is less than pivot value
                     __magic_name__      				=    new_pivot_index + 1
                     __magic_name__      				=    a[new_pivot_index]
                     __magic_name__      				=    a[index]
                     __magic_name__      				=    temp

       __magic_name__      				=    a[new_pivot_index + 1]
       __magic_name__      				=    a[end]
       __magic_name__      				=    temp
       return new_pivot_index + 1, count


snake_case : str     		=		TemporaryFile()
snake_case : Optional[int]     		=		1_0_0  # 1000 elements are to be sorted


snake_case		,			snake_case : Any     		=		0, 1  # mean and standard deviation
snake_case : Any     		=		np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
snake_case : str     		=		np.load(outfile)
snake_case : str     		=		len(M) - 1
snake_case : Tuple     		=		_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)






print(z)







"""simple docstring"""
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(a_)							,	"""Tatoeba directory does not exist.""")
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @cached_property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           __magic_name__      				=    tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase_ )







    @slow
    def 				a__			(		self :	List[str] ):





           '''simple docstring'''

           self.resolver.convert_models(['heb-eng'] )







    @slow
    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__				,  __magic_name__      				=    self.resolver.write_model_card('opus-mt-he-en'				,    dry_run=UpperCamelCase_ )
           assert mmeta["long_pair"] == "heb-eng"